基于高斯拉普拉斯算子的加权引导图滤波立体匹配算法
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摘要
提出了一种基于高斯拉普拉斯(LoG)算子的加权引导图滤波立体匹配算法。采用加权绝对差与梯度相融合的方式计算匹配代价。利用基于LoG算子的引导图滤波进行代价聚合,使惩戒参数能够随图像中的不同纹理区域进行自适应调整。使用赢者通吃(WTA)策略计算视差,并采用二次插值法得到最终的视差图。对Middlebury平台中的图像进行测试,结果表明,所提算法的平均误匹配率为4.32%。该算法可以有效地兼顾图像的高纹理区域和低纹理区域,并降低了视差图的误匹配率。
A stereo-matching algorithm using weighted guided image filtering based on the Laplacian of Gaussian(LoG)operator is proposed.The algorithm calculates matching cost by fusing weighted absolute difference and gradient.Then,cost aggregation is implemented using an improved guided image filtering based on the LoG operator to ensure that the penalty parameter is self-adaptive.The disparity computation is implemented using the winner-take-all(WTA)strategy,and the final disparity map is obtained using two different interpolation methods.The experimental results show that the average mismatch rate of the proposed algorithm on the Middlebury benchmark standard dataset is 4.32%.The proposed algorithm can process both high and low texture regions effectively;thus,the mismatch rate of the disparity map is reduced.
A stereo-matching algorithm using weighted guided image filtering based on the Laplacian of Gaussian(LoG)operator is proposed.The algorithm calculates matching cost by fusing weighted absolute difference and gradient.Then,cost aggregation is implemented using an improved guided image filtering based on the LoG operator to ensure that the penalty parameter is self-adaptive.The disparity computation is implemented using the winner-take-all(WTA)strategy,and the final disparity map is obtained using two different interpolation methods.The experimental results show that the average mismatch rate of the proposed algorithm on the Middlebury benchmark standard dataset is 4.32%.The proposed algorithm can process both high and low texture regions effectively;thus,the mismatch rate of the disparity map is reduced.
引文
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[3] Wang X F,Su Y Y,Tang L M,et al.A combined back and foreground-based stereo matching algorithm usingbeliefpropagationandself-adapting dissimilarity measure[J].International Journal of Pattern Recognition and Artificial Intelligence,2018,32(6):1850019.
[4] Mei X,Sun X,Zhou M C,et al.On building an accurate stereomatchingsystemongraphics hardware[C]∥2011IEEE International Conference on Computer Vision Workshops(ICCV Workshops),2011:467-474.
[5] Lu D,Lin X.Alocal stereo matching algorithm based on the combination of multiple similarity measures[J].Robot,2016,38(1):1-7.卢迪,林雪.多种相似性测度结合的局部立体匹配算法[J].机器人,2016,38(1):1-7.
[6] Fan H R,Yang F,Pan X R,et al.Stereo matching algorithm for improved Census transform and gradient fusion[J].Acta Optica Sinica,2018,38(2):0215006.范海瑞,杨帆,潘旭冉,等.一种改进Census变换与梯度融合的立体匹配算法[J].光学学报,2018,38(2):0215006.
[7] Zhang K,Lu J B,Lafruit G.Cross-based local stereo matching using orthogonal integral images[J].IEEE Transactions on Circuits and Systems for Video Technology,2009,19(7):1073-1079.
[8] Yoon K J,Kweon I S.Adaptive support-weight approach for correspondence search[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(4):650-656.
[9] Rhemann C,Hosni A,Bleyer M,et al.Fast costvolume filtering for visual correspondence and beyond[C]∥IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2011),2011:3017-3024.
[10] He K M,Sun J,Tang X O.Guided image filtering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(6):1397-1409.
[11] Scharstein D,Szeliski R.High-accuracy stereo depth maps using structured light[C]∥2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003:195-202.
[12] KolmogorovV, ZabihR. Computingvisual correspondencewithocclusionsusinggraph cuts[C]∥Proceedings Eighth IEEE International Conference on Computer Vision(ICCV 2001),2001:508-515.
[13] Humenberger M,Zinner C,Weber M,et al.A fast stereo matching algorithm suitable for embedded realtime systems[J]. Computer Vision and Image Understanding,2010,114(11):1180-1202.
[14] Tombari F,Mattoccia S,di Stefano L,et al.Near real-time stereo based on effective cost aggregation[C]∥2008 19th International Conference on Pattern Recognition,2008:1-4.
[15] Hirschmuller H. Accurate and efficient stereo processing by semi-global matching and mutual information[C]∥2005 IEEE Computer Society ConferenceonComputerVisionandPattern Recognition(CVPR 2005),2005:807-814.
[16] Kowalczuk J,Psota E T,Perez L C.Real-time stereo matching on CUDA using an iterative refinement method for adaptive support-weight correspondences[J].IEEE Transactions on Circuits and Systems for Video Technology,2013,23(1):94-104.
[17] Scharstein D,Pal C.Learning conditional random fields for stereo[C]∥2007 IEEE Conference on Computer Vision and Pattern Recognition,2007:1-8.
[18] Yang Q X.A non-local cost aggregation method for stereo matching[C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition,2012:1402-1409.